Objective: 1) identify all closely genetic pairs < x core variants 2) filter pairs that have mortality metadata (survived <-> died or survived <-> survived or died <-> died) 3) Extract all mutations/genes corresponding for this different “mortality switches/non-switches” 4) Identify all the mutations specific to survived <-> died switches (<=> mut_survived<->died - mut_survived<->survived (- mut_died<->died ?)) 5) From all closely related paired isolates filter the one that have been already phenotyped 6) Calculate changes (delta) for each measured phenotype (all GC and PI parameters) 7) Investigate difference in delta of all parameter changes for survived <-> died VS survived <-> survived (VS died<->died -> Is there a systematic significant change between theses parameters (absolute delta)? -> Are these changes directionnal (eg decrease in growth rate or AUC_cell_death) 8) Accessory question are there phenotypic changes not associated with detected genotypic changes -> should we investigate structural variants (nanopore pacbio ?)

Set/check knitR option and working directory

library(tidyverse)
## ── Attaching packages ──────────────────────────────────────────────────────────────────────────────────────── tidyverse 1.3.0 ──
## ✔ ggplot2 3.3.0     ✔ purrr   0.3.3
## ✔ tibble  2.1.3     ✔ dplyr   0.8.3
## ✔ tidyr   1.0.0     ✔ stringr 1.4.0
## ✔ readr   1.3.1     ✔ forcats 0.4.0
## ── Conflicts ─────────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
library(here)
## here() starts at /home/rguerillot/Documents/Travail/Abdou_project/Staph_infection_project/github_analysis/VANANZ_phenotypes
knitr::opts_chunk$set(echo = TRUE)
knitr::opts_knit$set(root.dir = here())
print(paste("My working directory is:" ,here()))
## [1] "My working directory is: /home/rguerillot/Documents/Travail/Abdou_project/Staph_infection_project/github_analysis/VANANZ_phenotypes"

Source functions

source("Functions/all_functions.R")

Import snp distance matrix (from snp-dist core.aln (snippy))

snp.dist.mat <- read.csv("Data_analysis/Genetic_pairs_analysis/VANANZ_core.aln.dist.mat", sep = "\t")  %>%
  as.matrix(.)

# put 1st column as row name and remove 1st column
row.names(snp.dist.mat) <- snp.dist.mat[,1]
snp.dist.mat <- snp.dist.mat[,2:845]

Turn distance matrix to long format

require(harrietr)
## Loading required package: harrietr
## Registered S3 method overwritten by 'treeio':
##   method     from
##   root.phylo ape
snp.dist.df <- harrietr::melt_dist(snp.dist.mat) %>%
  mutate(dist = as.integer(dist)) %>%
  filter(iso1 != "Reference") # remove reference
## Warning: attributes are not identical across measure variables;
## they will be dropped
hist(snp.dist.df$dist, breaks = 1000)

hist(snp.dist.df$dist, breaks = 100000, xlim = c(0,100))

Add rows of symetrical comparisons to close_strain.df to have all comparisons BPH1 <-> BPH2 and BPH2 <-> BPH1

close_strain_symetrical.df <- close_strain.df %>%
  mutate(isoA = iso1, isoB = iso2) %>%
  mutate(iso1 = isoB, iso2 = isoA) %>%
  select(iso1, iso2, dist) %>%
  rbind(close_strain.df, .)

Merge with strain metadata of each iso

ST_to_CC.df <- read.csv("Ideas_Grant_2020_analysis/Raw_data/Saureus_CC_to_ST.csv") 

# !!! use corrected mortality metadata, the following changes in the sample metadata table have been made:
# 1) all isolates recovered from patient who survived are now labeled "survived" (previously only first isolate)
# 2) only the last isolate from patient who died are labeled "died" (previously on l first) 

sample_meta_iso1.df <- read.csv("Ideas_Grant_2020_analysis/Raw_data/strain_metadata_corrected_mortality_with_controls.csv") %>%
  merge(., ST_to_CC.df, by = "ST", all.x = T) %>%
  select_all(.funs = funs(paste0("iso1_", .)))
## Warning: funs() is soft deprecated as of dplyr 0.8.0
## Please use a list of either functions or lambdas: 
## 
##   # Simple named list: 
##   list(mean = mean, median = median)
## 
##   # Auto named with `tibble::lst()`: 
##   tibble::lst(mean, median)
## 
##   # Using lambdas
##   list(~ mean(., trim = .2), ~ median(., na.rm = TRUE))
## This warning is displayed once per session.
sample_meta_iso2.df <- read.csv("Ideas_Grant_2020_analysis/Raw_data/strain_metadata_corrected_mortality_with_controls.csv") %>%
    merge(., ST_to_CC.df, by = "ST", all.x = T) %>%
  select_all(.funs = funs(paste0("iso2_", .)))

close_strain_symetrical.df <- merge(close_strain_symetrical.df, 
                         sample_meta_iso1.df,
                         by.x = "iso1",
                         by.y = "iso1_sample_id") %>%
                   merge(., 
                         sample_meta_iso2.df,
                         by.x = "iso2",
                         by.y = "iso2_sample_id") %>%
  mutate(CC = ifelse(is.na(iso1_CC), yes = as.character(iso2_CC), no = as.character(iso1_CC)))

Merge with strain phenotype data of each iso

sample_GC_param.df <- read.csv("Ideas_Grant_2020_analysis/Processed_data/Growth_curves/processed_median_parameters_GC.csv") %>%
  filter(strain_group != "CONTROL") %>%
  select(sample_id, ends_with("OD"))

sample_PI_param.df <- read.csv("Ideas_Grant_2020_analysis/Processed_data/PI_curves/processed_median_parameters_PI.csv") %>%
  filter(strain_group != "CONTROL") %>%
  select(sample_id, ends_with("death"))

# merge PI and GC param
sample_param.df <- merge(sample_GC_param.df, sample_PI_param.df)

# merge iso1 and iso2 pheno data with close_strain_symetrical.df
sample_param_iso1.df <- sample_param.df  %>%
  select_all(.funs = funs(paste0("iso1_", .)))

sample_param_iso2.df <- sample_param.df  %>%
  select_all(.funs = funs(paste0("iso2_", .)))


close_strain_symetrical_with_pheno.df <- merge(close_strain_symetrical.df, 
                         sample_param_iso1.df,
                         by.x = "iso1",
                         by.y = "iso1_sample_id") %>%
                   merge(., 
                         sample_param_iso2.df,
                         by.x = "iso2",
                         by.y = "iso2_sample_id")

Calculate delta and fold change between pairs and for each parameter

close_strain_symetrical_with_pheno_changes.df <- close_strain_symetrical_with_pheno.df %>%
  
  mutate(delta_time_of_max_rate_OD = iso2_time_of_max_rate_OD - iso1_time_of_max_rate_OD) %>%
  mutate(delta_max_rate_OD = iso2_max_rate_OD - iso1_max_rate_OD) %>%
  mutate(delta_doubling_time_OD = iso2_doubling_time_OD - iso1_doubling_time_OD) %>%
  mutate(delta_AUC_OD = iso2_AUC_OD - iso1_AUC_OD) %>%
  mutate(delta_time_of_max_OD = iso2_time_of_max_OD - iso1_time_of_max_OD) %>%
  mutate(delta_time_of_min_OD = iso2_time_of_min_OD - iso1_time_of_min_OD) %>%
  mutate(delta_max_OD = iso2_max_OD - iso1_max_OD) %>%
  mutate(delta_min_OD = iso2_min_OD - iso1_min_OD) %>%
  mutate(delta_end_point_OD = iso2_end_point_OD - iso1_end_point_OD) %>%
  
  mutate(delta_time_of_max_rate_death = iso2_time_of_max_rate_death - iso1_time_of_max_rate_death) %>%
  mutate(delta_max_rate_death = iso2_max_rate_death - iso1_max_rate_death) %>%
  mutate(delta_doubling_time_death = iso2_doubling_time_death - iso1_doubling_time_death) %>%
  mutate(delta_AUC_death = iso2_AUC_death - iso1_AUC_death) %>%
  mutate(delta_time_of_max_death = iso2_time_of_max_death - iso1_time_of_max_death) %>%
  mutate(delta_time_of_min_death = iso2_time_of_min_death - iso1_time_of_min_death) %>%
  mutate(delta_max_death = iso2_max_death - iso1_max_death) %>%
  mutate(delta_min_death = iso2_min_death - iso1_min_death) %>%
  mutate(delta_end_point_death = iso2_end_point_death - iso1_end_point_death) %>%
  
  mutate(log2fc_time_of_max_rate_OD = log2(iso2_time_of_max_rate_OD / iso1_time_of_max_rate_OD)) %>%
  mutate(log2fc_max_rate_OD = log2(iso2_max_rate_OD / iso1_max_rate_OD)) %>%
  mutate(log2fc_doubling_time_OD = log2(iso2_doubling_time_OD / iso1_doubling_time_OD)) %>%
  mutate(log2fc_AUC_OD = log2(iso2_AUC_OD / iso1_AUC_OD)) %>%
  mutate(log2fc_time_of_max_OD = log2(iso2_time_of_max_OD / iso1_time_of_max_OD)) %>%
  mutate(log2fc_time_of_min_OD = log2(iso2_time_of_min_OD / iso1_time_of_min_OD)) %>%
  mutate(log2fc_max_OD = log2(iso2_max_OD / iso1_max_OD)) %>%
  mutate(log2fc_min_OD = log2(iso2_min_OD / iso1_min_OD)) %>%
  mutate(log2fc_end_point_OD = log2(iso2_end_point_OD / iso1_end_point_OD)) %>%
  
  mutate(log2fc_time_of_max_rate_death = log2(iso2_time_of_max_rate_death / iso1_time_of_max_rate_death)) %>%
  mutate(log2fc_max_rate_death = log2(iso2_max_rate_death / iso1_max_rate_death)) %>%
  mutate(log2fc_doubling_time_death = log2(iso2_doubling_time_death / iso1_doubling_time_death)) %>%
  mutate(log2fc_AUC_death = log2(iso2_AUC_death / iso1_AUC_death)) %>%
  mutate(log2fc_time_of_max_death = log2(iso2_time_of_max_death / iso1_time_of_max_death)) %>%
  mutate(log2fc_time_of_min_death = log2(iso2_time_of_min_death / iso1_time_of_min_death)) %>%
  mutate(log2fc_max_death = log2(iso2_max_death / iso1_max_death)) %>%
  mutate(log2fc_min_death = log2(iso2_min_death / iso1_min_death)) %>%
  mutate(log2fc_end_point_death = log2(iso2_end_point_death / iso1_end_point_death))
## Warning: NaNs produced

## Warning: NaNs produced

## Warning: NaNs produced

Annotate mortality switches for all pairs

close_strain_symetrical_with_pheno_changes.df <- close_strain_symetrical_with_pheno_changes.df %>%
  mutate(switches = ifelse(iso1_mortality == "Survived" & iso2_mortality == "Died", "Survived-Died", NA)) %>%
  mutate(switches = ifelse(iso1_mortality == "Survived" & iso2_mortality == "Survived", "Survived-Survived", switches)) %>%
  mutate(switches = ifelse(iso1_mortality == "Died" & iso2_mortality == "Died", "Died-Died", switches)) %>%
  mutate(switches = ifelse(iso1_mortality == "Died" & iso2_mortality == "Survived", "Died-Survived", switches)) # %>%
  #select(iso1, iso2, iso1_mortality, iso2_mortality, switches)

Plots differences by switches

for (var in  grep("delta", colnames(close_strain_symetrical_with_pheno_changes.df), value = T)) {
  t <- ggviolin(data = close_strain_symetrical_with_pheno_changes.df,
          y = var,
          x = "switches",
          fill = "switches", add = "jitter"
          ) +
  theme_bw() +
  theme(legend.position = "none")+
  stat_compare_means(ref.group ="Survived-Survived",
                     method = "wilcox.test",
                     label = "p.signif")
  print(t)
}

remove duplicated pairs (for survived-survived and died-died)

close_strain_symetrical_with_pheno_changes_no_dup.df <- close_strain_symetrical_with_pheno_changes.df %>%
  rowwise() %>%
  mutate(key = paste(sort(c(iso1, iso2, switches)), collapse = "")) %>%
  #select(iso1, iso2, switches, key)
  distinct(key, .keep_all = T)
  
for (var in  grep("delta", colnames(close_strain_symetrical_with_pheno_changes_no_dup.df), value = T)) {
  
  t <- ggviolin(data = close_strain_symetrical_with_pheno_changes_no_dup.df,
          y = var,
          x = "switches",
          fill = "switches", add = "jitter"
          ) +
  theme_bw() +
  theme(legend.position = "none")+
  stat_compare_means(ref.group ="Survived-Survived",
                     method = "wilcox.test",
                     label = "p.signif")
  print(t)
  
}

# plot only survived-survived and survived-died


close_strain_symetrical_with_pheno_changes_no_dup.df <- close_strain_symetrical_with_pheno_changes_no_dup.df %>%
  filter(switches %in% c("Survived-Survived", "Survived-Died"))
  
for (var in  grep("delta", colnames(close_strain_symetrical_with_pheno_changes_no_dup.df), value = T)) {
  
  t <- ggboxplot(data = close_strain_symetrical_with_pheno_changes_no_dup.df,
          y = var,
          x = "switches",
          fill = "switches", add = "jitter"
          ) +
  theme_bw() +
  theme(legend.position = "none")+
  stat_compare_means(ref.group ="Survived-Survived",
                     method = "wilcox.test",
                     label = "p.signif")
  print(t)
  
}

for (var in  grep("log2fc", colnames(close_strain_symetrical_with_pheno_changes.df), value = T)) {
  
  t <- ggboxplot(data = close_strain_symetrical_with_pheno_changes_no_dup.df,
          y = var,
          x = "switches",
          fill = "switches", add = "jitter"
          ) +
  theme_bw() +
  theme(legend.position = "none")+
  stat_compare_means(ref.group ="Survived-Survived",
                     method = "wilcox.test",
                     label = "p.signif")
  print(t)
  
}

## Warning: Removed 56 rows containing non-finite values (stat_boxplot).
## Warning: Removed 56 rows containing non-finite values (stat_compare_means).
## Warning: Removed 29 rows containing missing values (geom_point).

## Warning: Removed 9 rows containing non-finite values (stat_boxplot).
## Warning: Removed 9 rows containing non-finite values (stat_compare_means).
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## Warning: Removed 3 rows containing non-finite values (stat_boxplot).
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## Warning: Removed 15 rows containing non-finite values (stat_boxplot).
## Warning: Removed 15 rows containing non-finite values (stat_compare_means).
## Warning: Removed 1 rows containing missing values (geom_point).

## Warning: Removed 35 rows containing non-finite values (stat_boxplot).
## Warning: Removed 35 rows containing non-finite values (stat_compare_means).
## Warning: Removed 35 rows containing missing values (geom_point).

Check by CC

for (var in  grep("delta", colnames(close_strain_symetrical_with_pheno_changes.df), value = T)) {
  
  t <- ggboxplot(data = close_strain_symetrical_with_pheno_changes_no_dup.df %>% filter(CC %in% c("CC1", "CC22", "CC8")),
          y = var,
          x = "switches",
          fill = "switches", add = "jitter", facet.by = "CC", 
          ) +
  theme_bw() +
  theme(legend.position = "none")+
  stat_compare_means(ref.group ="Survived-Survived",
                     method = "wilcox.test",
                     label = "p.signif")
  print(t)
  
}
## Warning: Computation failed in `stat_compare_means()`:
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for (var in  grep("log2fc", colnames(close_strain_symetrical_with_pheno_changes.df), value = T)) {
  
  t <- ggboxplot(data = close_strain_symetrical_with_pheno_changes_no_dup.df %>% filter(CC %in% c("CC1", "CC22", "CC8")),
          y = var,
          x = "switches",
          fill = "switches", add = "jitter", facet.by = "CC", 
          ) +
  theme_bw() +
  theme(legend.position = "none")+
  stat_compare_means(ref.group ="Survived-Survived",
                     method = "wilcox.test",
                     label = "p.signif")
  print(t)
  
}
## Warning: Computation failed in `stat_compare_means()`:
## argument "x" is missing, with no default

## Warning: Computation failed in `stat_compare_means()`:
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## Warning: Removed 26 rows containing non-finite values (stat_boxplot).
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Check phenotypic variation for pair with 0 SNP distance

for (var in  grep("delta", colnames(close_strain_symetrical_with_pheno_changes.df), value = T)) {
  
  t <- ggviolin(data = close_strain_symetrical_with_pheno_changes_no_dup.df %>% filter(dist == 0),
          y = var,
          x = "switches",
          fill = "switches", add = "jitter", label = "iso1"
          ) +
  theme_bw() +
  theme(legend.position = "none")+
  stat_compare_means(ref.group ="Survived-Survived",
                     method = "wilcox.test",
                     label = "p.signif")
  print(t)
}  
## Warning: Computation failed in `stat_compare_means()`:
## argument "x" is missing, with no default

## Warning: Computation failed in `stat_compare_means()`:
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## Warning: Computation failed in `stat_compare_means()`:
## argument "x" is missing, with no default

## Warning: Computation failed in `stat_compare_means()`:
## argument "x" is missing, with no default

## Warning: Computation failed in `stat_compare_means()`:
## argument "x" is missing, with no default

## Warning: Computation failed in `stat_compare_means()`:
## argument "x" is missing, with no default

## Warning: Computation failed in `stat_compare_means()`:
## argument "x" is missing, with no default

## Warning: Computation failed in `stat_compare_means()`:
## argument "x" is missing, with no default

## Warning: Computation failed in `stat_compare_means()`:
## argument "x" is missing, with no default

## Warning: Computation failed in `stat_compare_means()`:
## argument "x" is missing, with no default

## Warning: Computation failed in `stat_compare_means()`:
## argument "x" is missing, with no default

## Warning: Computation failed in `stat_compare_means()`:
## argument "x" is missing, with no default

## Warning: Computation failed in `stat_compare_means()`:
## argument "x" is missing, with no default

## Warning: Computation failed in `stat_compare_means()`:
## argument "x" is missing, with no default

## Warning: Computation failed in `stat_compare_means()`:
## argument "x" is missing, with no default

## Warning: Computation failed in `stat_compare_means()`:
## argument "x" is missing, with no default